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Learn Machine Learning by Doing Learn Now

Publish on LearnDataSci

LearnDataSci is on a mission to democratize data science and machine learning knowledge online, and we’d love for you to help us in our mission by publishing incredible educational content

Article inspiration

If you don’t have anything written but would really like to contribute here’s some common sources of inspiration:

Recent academic papers

In an intuitive way describe how something was done and replicate the code to achieve the same result. Distill important research into a more compact and easier to digest format.

A project you’ve worked on

Whether it was a hobby or for work, past projects are a great source of interesting articles. It will also force you to revisit, refactor, and organize your old code for presentation.

Something that took you forever to figure out

Did something finally click? Write down how and why you had a revelation in data science and help spark that understanding in others.

Data science topics

There’s an incredible amount of information beginner’s need to learn to get into data science, and it’s not all about machine learning. Some inspiration:

  • Explain a math concept from statistics, probability, linear algebra, calculus, or optimization that is important in data science
  • Walk through a data collection method, like web scraping, working with APIs, or some other unique method
  • Write an intuitive explanation of why a certain ML algorithm works and give examples of its use
  • Discuss ways of visualizing data more effectively in certain situations

Anything else that could be beneficial to someone that will start searching for a job would also be helpful. What do you wish you would have known? What article do you wish was written when you were starting out? Write about that!

How to get started

  1. Write — Create an engaging and unique article that makes a topic, technique, or skill in data science extremely accessible and intuitive. If you choose to write about a common topic, make your article is significantly better than anything else out there. Additionally, articles should heavily incorporate visuals and/or animations to explain some of the more complicated areas.

  2. Pitch — Host your article on Google Colab and share a link with our editor through the form below. Whether your article is accepted or not, you’ll receive a status report via email within a day or two.

  3. Edit — The curse of knowledge can blind us, so articles for LearnDataSci are reviewed thoroughly by an editor and student of data science. Incorporating a learner’s feedback is what takes an article from good to exceptional. If your article is accepted, an editor and student will carefully read through your article and provide feedback. Make any necessary edits and submit a final draft.

  4. Finalize — Once a final draft is submitted, our editors may add additional images, videos, links, and other resources to finalize the content before publication.

  5. Publish — Your article is published to LearnDataSci and distributed to our email list and social channels. Now’s a good time to share your work! Post the article to your social accounts, subreddits (r/datascience, r/pystats, r/machinelearning, etc.). Hacker News, and any other places you frequent.


Proposed articles should be unique and unpublished elsewhere. Although there’s no restrictions on reposting your article after we publish, we do ask you wait just one week until cross-posting to other outlets.

Jupyter notebooks

All articles should be written in Jupyter notebooks using proper Markdown and posted to Google Colab for sharing with our editors and students. All math should be presented using LaTeX in Markdown cells of the notebook. Assets, like images, videos, GIFs, or helper Python scripts, should be included with the notebook

Python and PEP 8

Currently we’re only accepting Python articles. Please follow the PEP 8 standards for Python. Our articles are sometimes the first point of contact for new programmers so our Python needs to be presented in a clean, organized, and and presented in a standardized way.

Data use

Articles that use illegally collected data, or data that’s normally behind a user account or paywall, will be turned away. Please include any data you’re using with the article submission.


Try finding or creating, images, videos, and other resources to help readers learn more effectively. Many readers click away when faced with giant walls of text. Media can help explain certain concepts much better than words alone. If using media that is not your own, please attribute the creator by providing a link to the source.

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